The minimum covariance determinant (MCD) scatter estimator is a highly robust estimator for the dispersion matrix of a multivariate, elliptically symmetric distribution. It is relatively fast to compute and intuitively appealing. In this note we derive its influence function and compute the asymptotic variances of its elements. A comparison with the one step reweighted MCD and with S-estimators is made. Also finite-sample results are reported. (C) 1999 Academic Press AMS 1991 subject classifications: 62F35, 62G35.status: publishe
Vis uri et al. (20Gl) proposed and illustrated the use ofthe affine equivariant rank covariance matr...
AbstractIn modern statistics the robust estimation of parameters is a central problem, i.e., an esti...
Consistency is shown for the minimum covariance determinant (MCD) estimators of multivariate locatio...
The minimum covariance determinant (MCD) scatter estimator is a highly robust estimator for the disp...
The minimum covariance determinant (MCD) scatter estimator is a highly robust estimator for the disp...
The minimum covariance determinant (MCD) estimators of multivariate location and scatter are robust ...
We define the minimum covariance determinant functionals for multivariate location and scatter throu...
The minimum covariance determinant (MCD) estimator of scatter is one of the most famous robust proce...
In Cator and Lopuhaä (arXiv:math.ST/0907.0079) [3], an asymptotic expansion for the minimum covarian...
AbstractIn Cator and Lopuhaä (arXiv:math.ST/0907.0079) [3], an asymptotic expansion for the minimum ...
In this paper, the influence functions and limiting distributions of the canonical correlations and ...
Visuri, Koivunen and Oja (2003) proposed and illustrated the use of the affine equivariant rank cova...
AbstractIn this paper, the influence functions and limiting distributions of the canonical correlati...
The minimum covariance determinant (MCD) method of Rousseeuw (1984) is a highly robust estimator of ...
The minimum covariance determinant (MCD) approach estimates the location and scatter matrix using th...
Vis uri et al. (20Gl) proposed and illustrated the use ofthe affine equivariant rank covariance matr...
AbstractIn modern statistics the robust estimation of parameters is a central problem, i.e., an esti...
Consistency is shown for the minimum covariance determinant (MCD) estimators of multivariate locatio...
The minimum covariance determinant (MCD) scatter estimator is a highly robust estimator for the disp...
The minimum covariance determinant (MCD) scatter estimator is a highly robust estimator for the disp...
The minimum covariance determinant (MCD) estimators of multivariate location and scatter are robust ...
We define the minimum covariance determinant functionals for multivariate location and scatter throu...
The minimum covariance determinant (MCD) estimator of scatter is one of the most famous robust proce...
In Cator and Lopuhaä (arXiv:math.ST/0907.0079) [3], an asymptotic expansion for the minimum covarian...
AbstractIn Cator and Lopuhaä (arXiv:math.ST/0907.0079) [3], an asymptotic expansion for the minimum ...
In this paper, the influence functions and limiting distributions of the canonical correlations and ...
Visuri, Koivunen and Oja (2003) proposed and illustrated the use of the affine equivariant rank cova...
AbstractIn this paper, the influence functions and limiting distributions of the canonical correlati...
The minimum covariance determinant (MCD) method of Rousseeuw (1984) is a highly robust estimator of ...
The minimum covariance determinant (MCD) approach estimates the location and scatter matrix using th...
Vis uri et al. (20Gl) proposed and illustrated the use ofthe affine equivariant rank covariance matr...
AbstractIn modern statistics the robust estimation of parameters is a central problem, i.e., an esti...
Consistency is shown for the minimum covariance determinant (MCD) estimators of multivariate locatio...